Course Details

Course Code(s):
PDNALPTPAD
Available:
Part-Time
Intake:
Autumn/Fall
Course Start Date:
September
Duration:
1 Year, Part-Time
Award:
Professional Diploma
Qualification:
NFQ Level 9 Minor Award
Faculty: Electronic & Computer Engineering, Science and Engineering
Course Type: Taught, Professional/Flexible
Fees: For Information on Fees, see section below.

Contact(s):

Name: Dr Arash Joorabchi
Address: Department of Electronic & Computer Engineering Email: Arash.Joorabchi@ul.ie
Express Interest

Register your interest here for more information or to be notified when applications are open.

Brief Description

This course qualifies for 80% funding under the HCI Fees Subsidy. Check fees section for details and eligibility.

With the growing amount of data in digital form, skills in NLP have become crucial for businesses to extract insights from written and spoken human language. For example, Generative AI, such as ChatGPT, is causing a stir in industry, both in its challenges and opportunities. 

Our Professional Diploma in Natural Language Processing is for those who want to deep dive into this specialist field for the expansive career opportunities it presents. You could be a graduate of a Computer Science or IT programme who wants to specialise in this space; or a software developer, data analyst, or language specialist who wants to upskill in the field of NLP. It’s also for engineers who want to learn the latest deep learning and natural language processing technology to benefit their businesses.

This UL@Work programme is co-designed with industry to ensure skills transfers are industry-responsive for current and future needs. Our programmes are developed for working professionals and are designed to be flexible and accessible. You can take it online, at your own pace, and from anywhere worldwide.

Build Your Master Plan: Stack Your Way to Success: If you are using this Professional Diploma as the final 30 ECTS Credits to stack into our Master of Professional Practice (MPP) you will need to apply via the MPP page HERE.

Applicants eligible for ICT Skillnet funding must apply in the Summer for the Sept start. 

On this NFQ Level 9 programme, you'll explore natural language processing frameworks to understand their core features and usability. You'll design code to implement solutions to a range of NLP-related problems in your workplace and learn how to use the right technologies, frameworks, and platforms to build natural language processing solutions that work well.

NLP skills are in high demand right across. For example, businesses need NLP to understand customer feedback, monitor brand reputation, and analyze social media interactions. In healthcare, NLP can be used to extract insights from electronic health records and medical literature.

The skills you'll acquire from this Professional Diploma in NLP are transferable to various job roles. Here are a few examples:

  • Data Scientist - In this role, you'll use NLP to extract insights from text data and build predictive models. You'll work with large datasets and use statistical methods to find patterns in the data.
     
  • Machine Learning Engineer - As a machine learning engineer, you'll work on developing algorithms that enable machines to learn from text data. You'll collaborate with data scientists and software engineers to build and deploy NLP models.
     
  • Computational Linguist - In this role, you'll work on developing and improving NLP algorithms. You'll use your understanding of human language to improve the accuracy of NLP models and make them more efficient.

Stack your learning with Microcreds:
Not quite ready to commit to a full professional diploma? Consider trying a microcredential. You can stack your microcred credits towards a professional diploma at a later date. Modules listed within the Programme Content with an (M) beside them are MicroCreds and can be taken independently.

Download the programme brochure HERE

Semester 1  

Introduction to Natural Language Processing: (M)
This module introduces you to the world of Natural Language Processing (NLP). We cover the fundamentals of statistical NLP and its techniques and applications with a foundational approach. 

Information Retrieval
This module offers an overview of the fields of Information Retrieval, Information Extraction, and Semantic Web. The module will cover a blend of fundamental concepts and current tools, techniques, and technologies used in modern information retrieval systems. 
 

Semester 2  

Advanced Natural Language Processing
This module covers advanced-level topics in natural language processing, focusing on deep learning-based approaches. These include text classification, synthetic parsing, part-of-speech tagging, named-entity recognition, coreference resolution, and machine translation. You will be taken through neural network architectures, including convolutional neural networks, recurrent neural networks, to long short-term memory networks (LSTMs).

Natural Language Understanding:
This module explores the field of Natural Language Understanding and related topics, including sentiment analysis, relation extraction, natural language inference, semantic parsing, question answering, language generation, and large language models like ChatGPT and conversational agents. 
 
 

Both Semesters

Future Focused Professional Portfolio 1 & 2:
In the first module, you will be led through a series of talks about the future of technology, the future of markets, and the future of society as a whole. You'll work collaboratively to identify key trends impacting your role and organisation. You'll also build a professional network and use it to reach out to key thought leaders in this area.

The second module will provide you with an opportunity to demonstrate independent and self-determined learning through the creation of your own individual portfolio. Your portfolio includes various activities that will show how you've improved your reflective practice, how well you've used discipline-specific knowledge in different situations, and how you've led a discussion about the future of your field.

 

The principal entry requirement is a Level 8 honours degree, at minimum second class honours (NFQ or other internationally recognised equivalent), in a relevant engineering, computing, mathematics, science or technology discipline.

Alternative Entry Route:

In accordance with the University's policy on the Recognition of Prior Learning, candidates who do not meet the minimum entry criteria may be considered. Applicants from other disciplines who have a relevant mathematics and computing element in their primary degree will also be considered.   Applicants who possess an honours u/g degree, minimum second-class, or equivalent in a non-numerate discipline and have a minimum of 3 years experiential learning in an appropriate computing discipline will be considered. English Language Requirements, right to shortlist, interview, and RPL policy will apply.

What to include with your application:

  • Qualification transcripts and certificates
  • A copy of your birth certificate or passport
  • A copy of your CV
  • If your qualifications have been obtained in a country where English is an official language this will suffice
  • If this is not available, the following additional documents must be provided:
  • English translation of your qualification(s)/transcripts AND
  • English language competency certificate
  • Please click here for Further Information on English Language Requirements

EU - €3,500 per annum 

Non EU - €4,750 per annum 

HCI Fees Subsidy - Candidates who satisfy the eligibility criteria can qualify for 80% funding subject to the availability of places. To clarify eligibility please go to Eligibility Criteria 

Further information on fees and payment of fees is available from the Student Fees Office website. All fee related queries should be directed to the Student Fees Office (Phone: +353 61 213 007 or email student.fees.office@ul.ie.)

 

Please click here for information on funding and scholarships.

FREQUENTLY ASKED QUESTIONS

Who is this Professional Diploma in NLP for? 
This programme is for anyone who wants to take a deep dive into the field of Natural Language Processing. You could be a recent graduate of a Computer Science/IT programme who wants to specialise in NLP; or a software developer, data analyst, or even a language specialist who wants to upskill their knowledge in the field of NLP. This is also suited to engineers who want to upskill on the latest in deep learning and natural language processing technology to benefit their businesses. 

Why should you/your employee sign up?
This programme is co-designed with industry and takes you on a journey into the NLP world. The journey starts with learning the fundamentals of the classic NLP in the first semester. It ends with introducing new state-of-the-art developments in the field based on recent advances in deep learning. It has been designed to be accessible to a variety of learners and backgrounds. 

The University of Limerick has an international reputation for research excellence leveraging industry experience. You will learn about the field and become an effective practitioner of its tools. You will work with the other learners on deadline-oriented exercises that reflect the type of collaboration that makes you effective in the industry. This will equip you with a strengthened curriculum vitae with practical experience from a renowned institution, accelerating your career and your return on investment. 

What skills will you and your employer benefit from on completing this programme? 
On successful completion, you will have the following:

  • A broad understanding of NLP, including its main tasks and core concepts and techniques, such as text classification, sentiment analysis, text summarisation, named entity recognition, and evaluation methods and datasets.
  • Hands-on experience with real-world applications of NLP, such as text categorisation, language modelling, spell checking, text similarity, information retrieval, chatbots, and machine translation.
  • Programming skills and experience of self-developed application examples in the Python language to design and develop a wide range of NLP solutions using a variety of ML/NLP libraries, frameworks, and platforms, such as NLTK, scikit-learn, spaCy, TextBlob, Gensim, PyTorch, Keras, Tensorflow, Hugging Face Model Hub, Google Colab and GitLab
  • An internationally recognised qualification from an eminent institution which will boost your career prospects.

What roles will you be qualified to pursue on completing this programme?

  • Artificial Intelligence (AI)/Machine Learning (ML) Engineer 
  • Natural Language Processing (NLP) Engineer
  • Software Development and Engineering roles which involve deploying innovative NLP and ML technologies. 
  • Technical leadership roles where decisions about using natural language processing and related deep learning are made.
  • Technical team member in realising new or augmented applications using natural language processing.

What programmes/platforms will we be using?

  • Python language to design and develop a wide range of NLP solutions using a variety of ML/NLP libraries and frameworks.
  • Platforms such as NLTK, scikit-learn, spaCy, TextBlob, Gensim, PyTorch, Keras, Tensorflow, Hugging Face Model Hub, Google Colab and GitLab.

Can you give an example of an assignment?
Assignment - Transformers Task: You work for a large company and the company receives 8 letters regarding one of your products. These letters range from letters of complaint to letters of praise.

  • Using Hugging Face Transformers, develop an automatically generated MS Word report for your manager on the letters regarding the product. Divide the report into a section on the positive responses and negative responses. Summarize the sentiments towards the product and provide a summary of the letters that reflect the most extreme sentiments. Determine and report in each case on what the customer wants to happen by automatically questioning their respective letters.
  • Knowing how each customer feels, automatically generate replies to the two most extreme examples, including their name in the greeting in the response.
  • In your code to generate the report for your manager and the letters of reply, indicate your design choices and use any pre-processing trick that you anticipate will improve your automatic generation.

How are learners assessed on this programme? 
There are no terminal exams on this programme. Assessment will be continuous; you will be asked to prepare a media plan that will be developed for your chosen company.

When does this programme start? 
This programme starts in September 2023 and finishes in May 2024. It is part-time and delivered online. 

How many hours per week? And what is the split between lecture and tutorial time? 
All lectures will be recorded. Live sessions will be at a time suitable to the student group. Online content should be accessed daily. Approx 13 hours per week, which breaks down as:

  • 2hrs online classes 
  • 2hrs e-moderated groups
  • 9 hrs self-study, as a guide.

How are learners assessed on this programme? 
Assessment consists of continuous assignments, quizzes, and/or final exams.

Course Director Profiles
Dr Arash Joorabchi is a lecturer in the Department of Electronic & Computer Engineering at the University of Limerick. Dr Joorabchi’s main research areas are Text Analytics and Natural Language Processing. He has worked on various research and industry-related projects in Health Informatics, Dialog Systems, and Digital Libraries.

Prof Patrick Denny is in the Department of Electronic and Computer Engineering at the University of Limerick and is an adjunct professor of engineering at the University of Galway. He has extensive industrial and academic experience with over 20 years of automotive engineering experience internationally as a senior engineer designing and developing radiofrequency and imaging systems for BMW, Daimler, Land Rover, Ford, VW, Audi, Volvo and other major car companies. Prof Denny has founded several conferences, working groups and standards bodies and has an extensive portfolio of patents, products and publications. His experience includes imaging systems, sensing, industrialisation, artificial intelligence and computer vision. 

Dr Pepijn van de Ven is a Senior Lecturer in Artificial Intelligence and Machine Learning and a Course Director for UL’s  MSc in Artificial Intelligence (AI), an industry-driven, fast-paced masters to upskill Ireland’s workforce in the use of Artificial Intelligence.